The surgeon : journal of the Royal Colleges of Surgeons of Edinburgh and Ireland · 2026
Validation and clinical utility of predictive nomograms for sentinel node positivity in cutaneous malignant melanoma in Ireland.
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Christodoulides N, Quirke N, Leon R, Ghinis A, Jeantet Q, Potter S
Аннотация
BACKGROUND: Sentinel lymph node biopsy (SLNB) is an important prognostic tool in cutaneous malignant melanoma. Nomograms such as those from Memorial Sloan Kettering (MSK), the Melanoma Institute of Australia (MIA), and LifeMath aim to predict SLNB positivity. We sought to compare and validate these tools in an Irish cohort.
METHODS: Clinical and pathological data were extracted from patient records to calculate predicted SLNB positivity using each of the three nomograms. Model performance was assessed for discrimination and calibration. Sensitivity, specificity, negative predictive value (NPV) and potential SLNB reduction were examined at thresholds of 5-15%. Decision curve analysis (DCA) was used to evaluate clinical utility.
RESULTS: Among 215 patients, 35 (16%) had a positive SLNB. All three models showed good predictive ability, with the MSK nomogram performing best. At a 5% threshold, DCA demonstrated minimal clinical benefit compared to a treat-all approach. In tumours with a predicted SLNB positivity risk of 10%, all three nomograms demonstrated clear net benefit and the potential to reduce unnecessary biopsies.
CONCLUSION: The MSK, MIA and LifeMath nomograms are well calibrated in an Irish melanoma cohort, with the MSK model showing the strongest performance. While limited at the 5% threshold, these tools may help refine patient selection for SLNB at a 10% threshold, acting as an adjunct to clinical decision-making and potentially reducing unnecessary procedures and associated morbidity.
METHODS: Clinical and pathological data were extracted from patient records to calculate predicted SLNB positivity using each of the three nomograms. Model performance was assessed for discrimination and calibration. Sensitivity, specificity, negative predictive value (NPV) and potential SLNB reduction were examined at thresholds of 5-15%. Decision curve analysis (DCA) was used to evaluate clinical utility.
RESULTS: Among 215 patients, 35 (16%) had a positive SLNB. All three models showed good predictive ability, with the MSK nomogram performing best. At a 5% threshold, DCA demonstrated minimal clinical benefit compared to a treat-all approach. In tumours with a predicted SLNB positivity risk of 10%, all three nomograms demonstrated clear net benefit and the potential to reduce unnecessary biopsies.
CONCLUSION: The MSK, MIA and LifeMath nomograms are well calibrated in an Irish melanoma cohort, with the MSK model showing the strongest performance. While limited at the 5% threshold, these tools may help refine patient selection for SLNB at a 10% threshold, acting as an adjunct to clinical decision-making and potentially reducing unnecessary procedures and associated morbidity.